{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# kNN算法\n",
    "## 2.2示例：使用k-近邻算法改进约会网站的配对效果\n",
    "#### 1.收集数据：提供文本文件\n",
    "#### 2.准备数据：使用Python解析文本文件\n",
    "#### 3.分析数据：使用Matplotlib画二维扩散图\n",
    "#### 4.训练算法：此步骤不适用于k-近邻算法\n",
    "#### 5.测试算法：使用海伦提供的部分数据作为测试样本\n",
    "#### 6.使用算法：产生简单的命令行程序，然后海伦可以输入一些特征数据以判断对方是否为自己喜欢的类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-1 k-近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import operator\n",
    "from os import listdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify0(inX, dataSet, labels, k): # inX 已知的一个样本类别\n",
    "    dataSetSize = dataSet.shape[0]  # numpy函数shape[0]返回dataSet的行数\n",
    "    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet  # #在列向量方向上重复inX共1次()，行向量方向上重复inX共dataSetSize次(纵向)\n",
    "    sqDiffMat = diffMat**2   #二维特征相减后平方   如：(x-x1)**2\n",
    "    sqDistances = sqDiffMat.sum(axis=1)\n",
    "    distances = sqDistances ** 0.5\n",
    "    sortedDistIndicies = distances.argsort()  # 返回distances中元素从小到大排序后的索引值\n",
    "    classCount={}   # 定一个记录类别次数的字典\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]   \n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1   #字典的get()方法,返回指定键的值,如果值不在字典中返回默认值\n",
    "    sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True) # 1 表示根据字典的值进行排序  reverse降序排序字典\n",
    "    return sortedClassCount[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-2将文本记录转化为Numpy的解析程序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def file2matrix(filename):\n",
    "    love_dictionary = {'largeDoses':3, 'smallDoses':2, 'didntLike':1}  # !! ValueError: invalid literal for int() with base 10: 'largeDoses'\n",
    "    fr = open(filename)   \n",
    "    arrayOfLines = fr.readlines() #读取文件内容\n",
    "    numberOfLines = len(arrayOfLines)  # 得到文件行数\n",
    "    returnMat = np.zeros((numberOfLines,3))  # 没有numpy,格式numpy.zeros( (m,n) )\n",
    "    classLabelVector = []  #返回的分类标签向量\n",
    "    index = 0  # 初始化行的索引值\n",
    "    for line in arrayOfLines:\n",
    "        line = line.strip()  # 删除空白符\n",
    "        listFromLine = line.split('\\t')  # 将字符串根据'\\t'分隔符进行切片。\n",
    "        returnMat[index, :] = listFromLine[0:3]   # 将数据前三列提取出来,存放到returnMat，得到特征矩阵\n",
    "        #classLabelVector.append(int(listFromLine[-1]))  # 把ifelse 语句换成此剧  （这是原始的句子）\n",
    "        if listFromLine[-1].isdigit():  # 判断切片后得到的数据最后一列 is num or not \n",
    "            classLabelVector.append(int(listFromLine[-1]))  # 是数字的话，添加到分类标签向量，像 [3,1,2,3,1,2] 数字代表含义见第三行\n",
    "        else:  \n",
    "            classLabelVector.append(love_dictionary.get(listFromLine[-1]))   # 不是数字的话，根据love_dictionary 的键 找对应的 值 [1,2,3]\n",
    "        \n",
    "        index += 1\n",
    "    return returnMat, classLabelVector   # 返回 特征矩阵， 分类向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.0920000e+04 8.3269760e+00 9.5395200e-01]\n",
      " [1.4488000e+04 7.1534690e+00 1.6739040e+00]\n",
      " [2.6052000e+04 1.4418710e+00 8.0512400e-01]\n",
      " ...\n",
      " [2.6575000e+04 1.0650102e+01 8.6662700e-01]\n",
      " [4.8111000e+04 9.1345280e+00 7.2804500e-01]\n",
      " [4.3757000e+04 7.8826010e+00 1.3324460e+00]]\n",
      "[3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')\n",
    "print(datingDataMat)\n",
    "print(datingLabels[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib  # 导入matplotlib\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "## ax.scatter(datingDataMat[:,1], datingDataMat[:,2])  # 取消注释看黑白结果\n",
    "ax.scatter(datingDataMat[:,1], datingDataMat[:,2], 15.0*np.array(datingLabels), 15.0*np.array(datingLabels))  # !!原始的是15.0*array()\n",
    "plt.show()\n",
    "# plt.show()  运行第一次出现 图所在的位置， 运行第二次 出现可视化图像"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-3归一化特征值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def autoNorm(dataSet):\n",
    "    minVals = dataSet.min(0)\n",
    "    maxVals = dataSet.max(0)\n",
    "    ranges = maxVals - minVals\n",
    "    normDataSet = np.zeros(np.shape(dataSet))  # !! 原始   zeros(shape(dataSet))  返回dataSet的矩阵行列数\n",
    "    m = dataSet.shape[0]  # 返回dataSet的行数  0 表 行， 1 表 列\n",
    "    normDataSet = dataSet - np.tile(minVals, (m,1))  # !! 原始 /tile(ranges, (m,1))  # 原始值减去最小值\n",
    "    normDataSet = normDataSet/np.tile(ranges, (m,1))  # !! 原始 /tile(ranges, (m,1))  # 除以最大和最小值的差\n",
    "    return normDataSet, ranges, minVals  # 返回归一化数据结果,数据范围,最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normMat= [[0.44832535 0.39805139 0.56233353]\n",
      " [0.15873259 0.34195467 0.98724416]\n",
      " [0.28542943 0.06892523 0.47449629]\n",
      " ...\n",
      " [0.29115949 0.50910294 0.51079493]\n",
      " [0.52711097 0.43665451 0.4290048 ]\n",
      " [0.47940793 0.3768091  0.78571804]]\n",
      "ranges= [9.1273000e+04 2.0919349e+01 1.6943610e+00]\n",
      "minVals= [0.       0.       0.001156]\n"
     ]
    }
   ],
   "source": [
    "normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "print('normMat=',normMat)\n",
    "print('ranges=',ranges)\n",
    "print('minVals=',minVals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-4分类器针对约会网站的测试代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datingClassTest():\n",
    "    hoRatio = 0.10\n",
    "    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')  #  返回 特征矩阵， 分类向量\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)  # 返回归一化数据结果,数据范围,最小值\n",
    "    m = normMat.shape[0]  # 获得normMat的行数\n",
    "    numTestVecs = int(m*hoRatio)\n",
    "    errorCount = 0.0  # 错误计数\n",
    "    for i in range(numTestVecs):   # 前numTestVecs个数据作为测试集\n",
    "        classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m,:], datingLabels[numTestVecs:m], 3)\n",
    "        print(\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, datingLabels[i]))\n",
    "        if classifierResult != datingLabels[i]:  # 判断机器分类结果如果出错， 则 errorCount 加 1\n",
    "            errorCount += 1.0\n",
    "    # print(\"the error is:\", errorCount)  # test the value of error\n",
    "    print(\"the total error rate is:%f\" % (errorCount/float(numTestVecs)))  # 打印错误率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the total error rate is:0.050000\n"
     ]
    }
   ],
   "source": [
    "datingClassTest()  # 太多行了 隐藏起来 点击空白处 查看结果    # 隐藏方法 Cell->Current Outputs->Toggle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-5约会网站预测函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifyPerson():\n",
    "    resultList = ['not at all', 'in small doses', 'in large doses']  # 输出结果\n",
    "    percentTats = float(input(\"percentage of time spent playing video game ?\"))  # 三维特征用户输入\n",
    "    ffMiles = float(input(\"frequent filer miles earned per year ?\"))  # 同上\n",
    "    iceCream = float(input(\"liters of ice cream consumed per year ?\"))  # 同上\n",
    "    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt') # 返回 特征矩阵， 分类向量\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)  # 返回归一化数据结果,数据范围,最小值\n",
    "    inArr = np.array([ffMiles, percentTats, iceCream])  # 生成NumPy数组，用于测试\n",
    "    classifierResult = classify0((inArr-minVals)/ranges,normMat, datingLabels,3)\n",
    "    print(\"you will probably like this person :\", resultList[classifierResult - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of time spent playing video game ?1000\n",
      "frequent filer miles earned per year ?1000\n",
      "liters of ice cream consumed per year ?10\n",
      "you will probably like this person : not at all\n"
     ]
    }
   ],
   "source": [
    "classifyPerson()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 示例：手写识别系统\n",
    "### 2.3.1 准备数据：将图像转化为测试向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify0(inX, dataSet, labels, k): # inX 已知的一个样本类别  注释同上\n",
    "    dataSetSize = dataSet.shape[0]\n",
    "    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet\n",
    "    sqDiffMat = diffMat**2\n",
    "    sqDistances = sqDiffMat.sum(axis=1)\n",
    "    distances = sqDistances ** 0.5\n",
    "    sortedDistIndicies = distances.argsort()\n",
    "    classCount={}\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1\n",
    "    sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedClassCount[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img2vector(filename):\n",
    "    returnVect =  np.zeros((1,1024))  # 将32x32的图像转化为1x1024的向量\n",
    "\n",
    "    fr = open(filename)\n",
    "    for i in range(32):\n",
    "        lineStr = fr.readline()\n",
    "        for j in range(32):\n",
    "            returnVect[0,32*i+j] = int(lineStr[j])\n",
    "    return returnVect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-6 手写数字识别系统的测试代码\n",
    "#### 使用KNN进行手写体识别的时候，先把数据文件和.ipy文件放在同一目录下，万一出现FileNotFoundError: [Errno 2] No such file or directory: ‘traingingDigits/0_0.txt’.\n",
    "#### 找不到文件的情况，则可以将程序中的文件路径改为绝对路径。 形式类似于'D:/xxx/xxx/xx/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handwritingClassTest():\n",
    "    hwLabels = []  \n",
    "    trainingFileList = os.listdir('C:/Users/DELL/pythonFolder/trainingDigits/')  # 不知道怎样加载文件夹（文本文件实在太多！！！） 答案见上\n",
    "    m = len(trainingFileList) # 获取训练集的长度\n",
    "    trainingMat = np.zeros((m,1024))  # 创建训练矩阵   m 行 1024 列     一行 代表一个训练文件  像  0_0.txt\n",
    "    for i in range(m):\n",
    "        fileNameStr = trainingFileList[i]\n",
    "        fileStr = fileNameStr.split('.')[0]\n",
    "        classNumStr = int(fileStr.split('_')[0])\n",
    "        hwLabels.append(classNumStr)\n",
    "        trainingMat[i,:] = img2vector('C:/Users/DELL/PythonFolder/trainingDigits/%s' % fileNameStr)\n",
    "    testFileList = os.listdir('C:/Users/DELL/PythonFolder/testDigits/')  \n",
    "    errorCount = 0.0   # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表(加载测试集)\n",
    "    mTest = len(testFileList) # 获取测试集的长度\n",
    "    for i in range(mTest):\n",
    "        fileNameStr = testFileList[i]\n",
    "        fileStr = fileNameStr.split('.')[0]\n",
    "        classNumStr = int(fileStr.split('_')[0])\n",
    "        vectorUnderTest = img2vector('C:/Users/DELL/PythonFolder/testDigits/%s' % fileNameStr)\n",
    "        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)\n",
    "        print(\"the classifierResult came back with: %d, the real answer is: %d\" % (classifierResult, classNumStr))\n",
    "        if classifierResult != classNumStr:\n",
    "            errorCount += 1.0\n",
    "    print(\"\\nthe total number of errors is: %d \" % errorCount)\n",
    "    print(\"\\nthe total error rate is: %f\" % (errorCount/float(mTest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
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      "the classifierResult came back with: 0, the real answer is: 0\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
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      "the classifierResult came back with: 1, the real answer is: 1\n",
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      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
      "the classifierResult came back with: 1, the real answer is: 1\n",
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      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 7, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "the classifierResult came back with: 9, the real answer is: 9\n",
      "\n",
      "the total number of errors is: 11 \n",
      "\n",
      "the total error rate is: 0.011628\n"
     ]
    }
   ],
   "source": [
    "handwritingClassTest()  # 太多行了 隐藏起来 点击空白处 查看结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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